class_docstr: AvgPool or MaxPool with BN. `pool_type` must be `max` or `avg`.
code: def __init__(
      self, pool_type, C, kernel_size, stride, padding, affine=True
  ):
      super().__init__()
      if pool_type.lower() == "max":
          self.pool = nn.MaxPool2d(kernel_size, stride, padding)
      elif pool_type.lower() == "avg":
          self.pool = nn.AvgPool2d(
              kernel_size, stride, padding, count_include_pad=False
          )
      else:
          raise ValueError()
  
      self.bn = nn.BatchNorm2d(C, affine=affine)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new PoolBN object from inherited base classes: nn with example: obj = PoolBN(pool_type, C, kernel_size, stride, padding, affine)